# DNN Filter Bank Cepstral Coefficients for Spoofing Detection

arXiv: https://arxiv.org/abs/1702.03791 · HTML: https://mlsanity.com/paper/1702.03791/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2017.

## Built on by (most cited first, 5 of 5)

- [FastAudio: A Learnable Audio Front-End for Spoof Speech Detection](https://mlsanity.com/paper/2109.02774/) (2021)
- [BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance](https://mlsanity.com/paper/2211.06987/) (2022)
- [Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise](https://mlsanity.com/paper/2211.01621/) (2022)
- [ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration](https://mlsanity.com/paper/2505.07709/) (2025)
- [AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worlds](https://mlsanity.com/paper/2509.04345/) (2025)

## Abstract (arXiv)

With the development of speech synthesis techniques, automatic speaker verification systems face the serious challenge of spoofing attack. In order to improve the reliability of speaker verification systems, we develop a new filter bank based cepstral feature, deep neural network filter bank cepstral coefficients (DNN-FBCC), to distinguish between natural and spoofed speech. The deep neural network filter bank is automatically generated by training a filter bank neural network (FBNN) using natural and synthetic speech. By adding restrictions on the training rules, the learned weight matrix of FBNN is band-limited and sorted by frequency, similar to the normal filter bank. Unlike the manually designed filter bank, the learned filter bank has different filter shapes in different channels, which can capture the differences between natural and synthetic speech more effectively. The experimental results on the ASVspoof {2015} database show that the Gaussian mixture model maximum-likelihood (GMM-ML) classifier trained by the new feature performs better than the state-of-the-art linear frequency cepstral coefficients (LFCC) based classifier, especially on detecting unknown attacks.